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Found 94 papers, 26 papers with code
Universal Fingerprint Generation: Controllable Diffusion Model with Multimodal Conditions
The utilization of synthetic data for fingerprint recognition has garnered increased attention due to its potential to alleviate privacy concerns surrounding sensitive biometric data.
Mobile Contactless Palmprint Recognition: Use of Multiscale, Multimodel Embeddings
Most prior work focuses on extracting global features or local features alone for palmprint matching, whereas this research introduces a novel framework that combines global and local features for enhanced palmprint matching accuracy.
AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems
We propose AdvGen, an automated Generative Adversarial Network, to simulate print and replay attacks and generate adversarial images that can fool state-of-the-art PADs in a physical domain attack setting.
Accelerated Fingerprint Enhancement: A GPU-Optimized Mixed Architecture Approach
One innovative element of this approach includes a novel Fingerprint Enhancement Gabor layer, specifically designed for GPU computations.
A Universal Latent Fingerprint Enhancer Using Transformers
This study aims to develop a fast method, which we call ULPrint, to enhance various latent fingerprint types, including those obtained from real crime scenes and laboratory-created samples, to boost fingerprint recognition system performance.
ViT Unified: Joint Fingerprint Recognition and Presentation Attack Detection
In this work, we leverage a vision transformer architecture for joint spoof detection and matching and report competitive results with state-of-the-art (SOTA) models for both a sequential system (two ViT models operating independently) and a unified architecture (a single ViT model for both tasks).
Child Palm-ID: Contactless Palmprint Recognition for Children
Using a contactless child palmprint database, Child-PalmDB1, consisting of 19, 158 images from 1, 020 unique palms (in the age range of 6 mos.
Latent Fingerprint Recognition: Fusion of Local and Global Embeddings
One of the most challenging problems in fingerprint recognition continues to be establishing the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene (i. e., latent prints or fingermarks).
Child PalmID: Contactless Palmprint Recognition
Developing and least developed countries face the dire challenge of ensuring that each child in their country receives required doses of vaccination, adequate nutrition and proper medication.
AFR-Net: Attention-Driven Fingerprint Recognition Network
combining the complimentary representations of attention-based and CNN-based embeddings for improved state-of-the-art (SOTA) fingerprint recognition (both authentication and identification).
Minutiae-Guided Fingerprint Embeddings via Vision Transformers
We further demonstrate that by guiding the ViT to focus in on local, minutiae related features, we can boost the recognition performance.
Learning an Ensemble of Deep Fingerprint Representations
While learning an ensemble of representations can mitigate this problem, two critical challenges need to be addressed: (i) How to extract multiple diverse representations from the same fingerprint image?
Synthetic Latent Fingerprint Generator
Given a full fingerprint image (rolled or slap), we present CycleGAN models to generate multiple latent impressions of the same identity as the full print.
On Demographic Bias in Fingerprint Recognition
Fingerprint recognition systems have been deployed globally in numerous applications including personal devices, forensics, law enforcement, banking, and national identity systems.
Fingerprint Template Invertibility: Minutiae vs. Deep Templates
We show that while a deep template can be inverted to produce a fingerprint image that could be matched to its source image, deep templates are more resistant to reconstruction attacks than minutiae templates.
SpoofGAN: Synthetic Fingerprint Spoof Images
This work aims to demonstrate the utility of synthetic (both live and spoof) fingerprints in supplying these algorithms with sufficient data to improve the performance of fingerprint spoof detection algorithms beyond the capabilities when training on a limited amount of publicly available real datasets.
AdaFace: Quality Adaptive Margin for Face Recognition
In this work, we introduce another aspect of adaptiveness in the loss function, namely the image quality.
Ranked #1 on
Surveillance-to-Booking
on IJB-S
RealGait: Gait Recognition for Person Re-Identification
Given that our experimental results show that current gait recognition approaches designed under data collected in controlled scenarios are inappropriate for real surveillance scenarios, we propose a novel gait recognition method, called RealGait.
PrintsGAN: Synthetic Fingerprint Generator
The publicly available datasets that do exist contain very few identities and impressions per finger.
Trustworthy AI: A Computational Perspective
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society.
Biometrics: Trust, but Verify
Over the past two decades, biometric recognition has exploded into a plethora of different applications around the globe.
FedFace: Collaborative Learning of Face Recognition Model
DNN-based face recognition models require large centrally aggregated face datasets for training.
C2CL: Contact to Contactless Fingerprint Matching
Matching contactless fingerprints or finger photos to contact-based fingerprint impressions has received increased attention in the wake of COVID-19 due to the superior hygiene of the contactless acquisition and the widespread availability of low cost mobile phones capable of capturing photos of fingerprints with sufficient resolution for verification purposes.
Unified Detection of Digital and Physical Face Attacks
Proposed UniFAD outperforms prevailing defense methods and their fusion with an overall TDR = 94. 73% @ 0. 2% FDR on a large fake face dataset consisting of 341K bona fide images and 448K attack images of 25 types across all 3 categories.
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images
During training, FaceGuard automatically synthesizes challenging and diverse adversarial attacks, enabling a classifier to learn to distinguish them from real faces and a purifier attempts to remove the adversarial perturbations in the image space.
Lifting 2D StyleGAN for 3D-Aware Face Generation
We propose a framework, called LiftedGAN, that disentangles and lifts a pre-trained StyleGAN2 for 3D-aware face generation.
To be Robust or to be Fair: Towards Fairness in Adversarial Training
However, we find that adversarial training algorithms tend to introduce severe disparity of accuracy and robustness between different groups of data.
Infant-ID: Fingerprints for Global Good
In many of the least developed and developing countries, a multitude of infants continue to suffer and die from vaccine-preventable diseases and malnutrition.
Transfer Learning in Deep Reinforcement Learning: A Survey
Reinforcement learning is a learning paradigm for solving sequential decision-making problems.
White-Box Evaluation of Fingerprint Recognition Systems
While a few studies have conducted white-box evaluations of the fingerprint reader, feature extractor, and matching components, no existing study has provided a full system, white-box analysis of the uncertainty introduced at each stage of a fingerprint recognition system.
Mitigating Face Recognition Bias via Group Adaptive Classifier
Our proposed group adaptive classifier mitigates bias by using adaptive convolution kernels and attention mechanisms on faces based on their demographic attributes.
Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approach
State-of-the-art spoof detection methods tend to overfit to the spoof types seen during training and fail to generalize to unknown spoof types.
DashCam Pay: A System for In-vehicle Payments Using Face and Voice
We present our ongoing work on developing a system, called DashCam Pay, that enables in-vehicle payments in a seamless and secure manner using face and voice biometrics.
Fingerprint Presentation Attack Detection: A Sensor and Material Agnostic Approach
The vulnerability of automated fingerprint recognition systems to presentation attacks (PA), i. e., spoof or altered fingers, has been a growing concern, warranting the development of accurate and efficient presentation attack detection (PAD) methods.
HERS: Homomorphically Encrypted Representation Search
We present a method to search for a probe (or query) image representation against a large gallery in the encrypted domain.
Child Face Age-Progression via Deep Feature Aging
Given a gallery of face images of missing children, state-of-the-art face recognition systems fall short in identifying a child (probe) recovered at a later age.
Boosting Unconstrained Face Recognition with Auxiliary Unlabeled Data
In recent years, significant progress has been made in face recognition, which can be partially attributed to the availability of large-scale labeled face datasets.
Towards Universal Representation Learning for Deep Face Recognition
Recognizing wild faces is extremely hard as they appear with all kinds of variations.
Fingerprint Spoof Detection: Temporal Analysis of Image Sequence
We utilize the dynamics involved in the imaging of a fingerprint on a touch-based fingerprint reader, such as perspiration, changes in skin color (blanching), and skin distortion, to differentiate real fingers from spoof (fake) fingers.
Fingerprint Synthesis: Search with 100 Million Prints
Evaluation of large-scale fingerprint search algorithms has been limited due to lack of publicly available datasets.
Universal Material Translator: Towards Spoof Fingerprint Generalization
Spoof detectors are classifiers that are trained to distinguish spoof fingerprints from bonafide ones.
Fingerprint Spoof Generalization
The proposed approach is shown to improve the generalization performance of a state-of-the-art spoof detector, namely Fingerprint Spoof Buster, from TDR of 75. 24% to 91. 78% @ FDR = 0. 2%.
Jointly De-biasing Face Recognition and Demographic Attribute Estimation
We address the problem of bias in automated face recognition and demographic attribute estimation algorithms, where errors are lower on certain cohorts belonging to specific demographic groups.
Finding Missing Children: Aging Deep Face Features
Given a gallery of face images of missing children, state-of-the-art face recognition systems fall short in identifying a child (probe) recovered at a later age.
Learning a Fixed-Length Fingerprint Representation
DeepPrint incorporates fingerprint domain knowledge, including alignment and minutiae detection, into the deep network architecture to maximize the discriminative power of its representation.
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for the three popular data types, i. e., images, graphs and text.
White-Box Evaluation of Fingerprint Matchers: Robustness to Minutiae Perturbations
While a few studies have conducted stand-alone evaluations of the fingerprint reader and feature extraction modules of fingerprint recognition systems, little work has been devoted towards white-box evaluations of the fingerprint matching module.
AdvFaces: Adversarial Face Synthesis
Face recognition systems have been shown to be vulnerable to adversarial examples resulting from adding small perturbations to probe images.
OCT Fingerprints: Resilience to Presentation Attacks
Optical coherent tomography (OCT) fingerprint technology provides rich depth information, including internal fingerprint (papillary junction) and sweat (eccrine) glands, in addition to imaging any fake layers (presentation attacks) placed over finger skin.
End-to-End Pore Extraction and Matching in Latent Fingerprints: Going Beyond Minutiae
Because pores are one of the most reliable features besides minutiae to identify latent fingerprints, we propose an end-to-end automatic pore extraction and matching system to analyze the utility of pores in latent fingerprint identification.
Recurrent Embedding Aggregation Network for Video Face Recognition
Recurrent networks have been successful in analyzing temporal data and have been widely used for video analysis.
IJB–S: IARPA Janus Surveillance Video Benchmark
We present IJB-S dataset, an open-source IARPA Janus Surveillance Video Benchmark and associated protocols.
Probabilistic Face Embeddings
Embedding methods have achieved success in face recognition by comparing facial features in a latent semantic space.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
Fingerprints: Fixed Length Representation via Deep Networks and Domain Knowledge
We learn a discriminative fixed length feature representation of fingerprints which stands in contrast to commonly used unordered, variable length sets of minutiae points.
Infant-Prints: Fingerprints for Reducing Infant Mortality
In developing countries around the world, a multitude of infants continue to suffer and die from vaccine-preventable diseases, and malnutrition.
Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN)
We propose a new approach to video face recognition.
Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone Users via Deep Temporal Features
Prevailing user authentication schemes on smartphones rely on explicit user interaction, where a user types in a passcode or presents a biometric cue such as face, fingerprint, or iris.
Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier
Prevailing fingerprint recognition systems are vulnerable to spoof attacks.
Learning Continuous Face Age Progression: A Pyramid of GANs
The two underlying requirements of face age progression, i. e. aging accuracy and identity permanence, are not well studied in the literature.
Fingerprint Presentation Attack Detection: Generalization and Efficiency
We study the problem of fingerprint presentation attack detection (PAD) under unknown PA materials not seen during PAD training.
WarpGAN: Automatic Caricature Generation
We propose, WarpGAN, a fully automatic network that can generate caricatures given an input face photo.
Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning
To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning.
DocFace+: ID Document to Selfie Matching
Numerous activities in our daily life require us to verify who we are by showing our ID documents containing face images, such as passports and driver licenses, to human operators.
DocFace: Matching ID Document Photos to Selfies
Numerous activities in our daily life, including transactions, access to services and transportation, require us to verify who we are by showing our ID documents containing face images, e. g. passports and driver licenses.
Altered Fingerprints: Detection and Localization
Fingerprint alteration, also referred to as obfuscation presentation attack, is to intentionally tamper or damage the real friction ridge patterns to avoid identification by an AFIS.
Latent Fingerprint Recognition: Role of Texture Template
We propose a texture template approach, consisting of a set of virtual minutiae, to improve the overall latent fingerprint recognition accuracy.
Automatic Latent Fingerprint Segmentation
We present a simple but effective method for automatic latent fingerprint segmentation, called SegFinNet.
Fingerprint Match in Box
We open source fingerprint Match in Box, a complete end-to-end fingerprint recognition system embedded within a 4 inch cube.
Matching Fingerphotos to Slap Fingerprint Images
We address the problem of comparing fingerphotos, fingerprint images from a commodity smartphone camera, with the corresponding legacy slap contact-based fingerprint images.
On the Intrinsic Dimensionality of Image Representations
This paper addresses the following questions pertaining to the intrinsic dimensionality of any given image representation: (i) estimate its intrinsic dimensionality, (ii) develop a deep neural network based non-linear mapping, dubbed DeepMDS, that transforms the ambient representation to the minimal intrinsic space, and (iii) validate the veracity of the mapping through image matching in the intrinsic space.
Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge
Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets.
RaspiReader: Open Source Fingerprint Reader
Finally, fingerprint matching experiments between images acquired from the FTIR output of RaspiReader and images acquired from a COTS reader verify the interoperability of the RaspiReader with existing COTS optical readers.
Fingerprint Spoof Buster
Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides state-of-the-art accuracies in fingerprint spoof detection for intra-sensor, cross-material, cross-sensor, as well as cross-dataset testing scenarios.
Learning Face Age Progression: A Pyramid Architecture of GANs
The two underlying requirements of face age progression, i. e. aging accuracy and identity permanence, are not well studied in the literature.
Longitudinal Study of Child Face Recognition
Face comparison scores are obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTS-A and FaceNet matchers.
On the Capacity of Face Representation
Numerical experiments on unconstrained faces (IJB-C) provides a capacity upper bound of $2. 7\times10^4$ for FaceNet and $8. 4\times10^4$ for SphereFace representation at a false acceptance rate (FAR) of 1%.
RaspiReader: An Open Source Fingerprint Reader Facilitating Spoof Detection
Finally, fingerprint matching experiments between images acquired from the FTIR output of the RaspiReader and images acquired from a COTS fingerprint reader verify the interoperability of the RaspiReader with existing COTS optical readers.
Patient Subtyping via Time-Aware LSTM Networks
We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes.
Ranked #4 on
Multivariate Time Series Forecasting
on USHCN-Daily
Automatic Face Image Quality Prediction
Face image quality can be defined as a measure of the utility of a face image to automatic face recognition.
Face Clustering: Representation and Pairwise Constraints
Given this representation, we design a clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly estimates the adjacency matrix only based on the similarity between face images.
Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach
In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes.
Ranked #5 on
Facial Attribute Classification
on LFWA
Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations
We present the design and manufacturing of high fidelity universal 3D fingerprint targets, which can be imaged on a variety of fingerprint sensing technologies, namely capacitive, contact-optical, and contactless-optical.
Automated Latent Fingerprint Recognition
Experimental results show that the rank-1 identification accuracies (query latent is matched with its true mate in the reference database) are 64. 7% for the NIST SD27 and 75. 3% for the WVU latent databases, against a reference database of 100K rolled prints.
On the Reconstruction of Face Images from Deep Face Templates
Our study demonstrates the need to secure deep templates in face recognition systems.
Asynchronous Multi-Task Learning
The models for each hospital may be different because of the inherent differences in the distributions of the patient populations.
Clustering Millions of Faces by Identity
Additionally, we present preliminary work on video frame clustering (achieving 0. 71 F-measure when clustering all frames in the benchmark YouTube Faces dataset).
Nonlinear Local Metric Learning for Person Re-identification
In this paper, we propose a nonlinear local metric learning (NLML) method to improve the state-of-the-art performance of person re-identification on public datasets.
Face Search at Scale: 80 Million Gallery
We evaluate the proposed face search system on a gallery containing 80 million web-downloaded face images.
Ranked #15 on
Face Verification
on IJB-A
Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A
Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search) and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations.
Biometrics for Child Vaccination and Welfare: Persistence of Fingerprint Recognition for Infants and Toddlers
However, persistence of biometric recognition accuracy has not been studied systematically for children in the age group of 0-4 years.
A Fast and Accurate Unconstrained Face Detector
First, a new image feature called Normalized Pixel Difference (NPD) is proposed.
Ranked #6 on
Face Detection
on PASCAL Face
Scalable Kernel Clustering: Approximate Kernel k-means
Kernel-based clustering algorithms have the ability to capture the non-linear structure in real world data.
Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning
One difficulty in learning the pairwise similarity measure is that there is a significant amount of noise and inter-worker variations in the manual annotations obtained via crowdsourcing.
Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition
Recent studies have shown that multiple kernel learning is very effective for object recognition, leading to the popularity of kernel learning in computer vision problems.
